Object Surface Identification Using Finger Vibration Information and Machine Learning in High-Precision Tracing Motion

This study proposes an object identification method that combines high-precision tracing motion by an industrial robot with vibration information obtained from a polyvinylidene fluoride (PVDF) film sensor mounted on the finger surface. In conventional tactile sensing technology, methods that place sensors on the contact surface face challenges such as reduced durability due to wear and limited design freedom for the finger surface. The proposed method overcomes these issues by exploiting vibration propagation to physically isolate the sensor from the contact point. In this paper, we first investigate how tracing speed and pressing depth affect the vibration spectrum based on the mechanism of vibration generation. We then verify, through fundamental experiments using sandpaper, how finger material influences vibration transmission characteristics. The results demonstrate that a lightweight resin finger is advantageous for acquiring high-frequency vibrations and that strict control of tracing speed is essential for ensuring identification accuracy. Based on these findings, we conducted identification experiments using wide-band vibration information from 0 Hz to 8,000 Hz as features. The experiments targeted 33 types of diverse objects selected from real-world environments according to onomatopoeic classification. Evaluation with a support vector machine achieved an extremely high identification rate exceeding 99% for both finely textured sandpaper and real-world objects under high-precision robot control. Furthermore, high identification rates were maintained even when the tracing position and pressing depth were varied randomly, demonstrating the effectiveness and robustness of the proposed method.